The Experts below are selected from a list of 78603 Experts worldwide ranked by ideXlab platform
Takashi Bando - One of the best experts on this subject based on the ideXlab platform.
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essential feature extraction of Driving Behavior using a deep learning method
IEEE Intelligent Vehicles Symposium, 2015Co-Authors: Hailong Liu, Kazuhito Takenaka, Tadahiro Taniguchi, Yusuke Tanaka, Takashi BandoAbstract:Driving Behavior can be represented by many different types of measured sensor information obtained through a control area network. We assume that the measured sensor information is generated from several hidden time-series data through multiple nonlinear transformations. These hidden time-series data are statistically independent of each other and capture essential Driving Behavior. Driving Behavior information is usually generated by multiple nonlinear transformations that fuse essential features, e.g., "Yaw rate" is generated by fusing the velocity of the vehicle and the change of Driving direction. However, Driving Behavior data is often redundant because such data includes multivariate information and involves duplicated essential features. In this paper, we propose a feature extraction method to extract essential features from redundant Driving Behavior data using a deep sparse autoencoder (DSAE), which is a deep learning method. Two-dimensional features are extracted from seven-dimensional artificial data using a DSAE and are determined experimentally to be highly correlated with the prepared essential features. DSAEs are also used to extract features from an actual Driving Behavior data set. To verify a DSAE's ability to extract essential Driving Behavior features and filter out redundant information, we prepare twelve data sets that include some or all of the Driving Behavior information. Twelve DSAEs are used to independently extract features from the twelve prepared data sets, and canonical correlation analysis is used to analyze the canonical correlation coefficients between extracted features. Furthermore, we verify DSAEs' ability to extract essential Driving Behavior features from the redundant Driving Behavior data sets.
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Unsupervised Hierarchical Modeling of Driving Behavior and Prediction of Contextual Changing Points
IEEE Transactions on Intelligent Transportation Systems, 2015Co-Authors: Tadahiro Taniguchi, Kazuhito Takenaka, Kentarou Hitomi, Shogo Nagasaka, Takashi BandoAbstract:An unsupervised learning method, called double articulation analyzer with temporal prediction (DAA-TP), is proposed on the basis of the original DAA model. The method will enable future advanced Driving assistance systems to determine Driving context and predict possible scenarios of Driving Behavior by segmenting and modeling incoming Driving-Behavior time series data. In previous studies, we applied the DAA model to Driving-Behavior data and argued that contextual changing points can be estimated as changing points of chunks. A sequence prediction method, which predicts the next hidden state sequence, was also proposed in a previous study. However, the original DAA model does not model the duration of chunks of Driving Behavior and is not able to do a temporal prediction of the scenarios. Our DAA-TP method explicitly models the duration of chunks of Driving Behavior on the assumption that Driving-Behavior data have a two-layered hierarchical structure, i.e., double articulation structure. For this purpose, the hierarchical Dirichlet process hidden semi-Markov model is used for explicitly modeling the duration of segments of Driving-Behavior data. A Poisson distribution is also used to model the duration distribution of Driving-Behavior segments. The duration distribution of chunks of Driving-Behavior data is also theoretically calculated using the reproductive property of the Poisson distribution. We also propose a calculation method for obtaining the probability distribution of the remaining duration of current Driving words as a mixture of Poisson distribution with a theoretical approximation for unobserved Driving words. This method can calculate the posterior probability distribution of the next termination time of chunks by explicitly modeling all probable chunking results for observed data. The DAA-TP was applied to a synthetic data set having a double articulation structure to evaluate its model consistency. To evaluate the effectiveness of DAA-TP, we applied it to a Driving-Behavior data set recorded at actual factory circuits. The DAA-TP could predict the next termination time of chunks more accurately than the compared methods. We also report the qualitative results for understanding the potential capability of DAA-TP.
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Towards prediction of Driving Behavior via basic pattern discovery with BP-AR-HMM
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: Ryunosuke Hamada, Takashi Bando, Takatomi Kubo, Kazushi Ikeda, Zujie Zhang, Tomohiro Shibata, Masumi EgawaAbstract:Prediction of Driving Behaviors is important problem in developing the next-generation Driving support system. In order to take account of diverse Driving situations, it is necessary to deal with multiple time series data considering commonalities and differences among them. In this paper we utilize the beta process autoregressive hidden Markov model (BP-AR-HMM) that can model multiple time series considering common and different features among them using the beta process as a prior distribution. We apply the BP-AR-HMM to actual Driving Behavior data to estimate VAR process parameters that represent the Driving Behaviors, and with the estimated parameters we predict the Driving Behaviors of unknown test data. The results suggest that it is possible to identify the dynamical Behaviors of Driving operations using BP-AR-HMM, and to predict Driving Behaviors in actual environment.
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drive video summarization based on double articulation structure of Driving Behavior
ACM Multimedia, 2012Co-Authors: Kazuhito Takenaka, Takashi Bando, Shogo Nagasaka, Tadahiro TaniguchiAbstract:This paper provides a novel summarization method for drive videos using Driving Behavior, such as driver maneuvers and vehicle reaction, recorded simultaneously alongside video. We segmented the Driving Behavior into chunks via an unsupervised manner and summarized the drive videos using the chunks, i.e., the switching points of the chunks were emphasized and the middle of the chunks were compressed. As the result of subjective evaluation, we found that the chunks were more consistent with human-recognized Driving context than the image-based method and that the summarized video was more suitable for reviewing entire Driving scenes, i.e., our method achieved an efficient summarization.
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semiotic prediction of Driving Behavior using unsupervised double articulation analyzer
IEEE Intelligent Vehicles Symposium, 2012Co-Authors: Tadahiro Taniguchi, Kentarou Hitomi, Shogo Nagasaka, Naiwala P Chandrasiri, Takashi BandoAbstract:In this paper, we propose a novel semiotic prediction method for Driving Behavior based on double articulation structure. It has been reported that predicting Driving Behavior from its multivariate time series Behavior data by using machine learning methods, e.g., hybrid dynamical system, hidden Markov model and Gaussian mixture model, is difficult because a driver's Behavior is affected by various contextual information. To overcome this problem, we assume that contextual information has a double articulation structure and develop a novel semiotic prediction method by extending nonparametric Bayesian unsupervised morphological analyzer. Effectiveness of our prediction method was evaluated using synthetic data and real Driving data. In these experiments, the proposed method achieved long-term prediction 2–6 times longer than some conventional methods.
Tadahiro Taniguchi - One of the best experts on this subject based on the ideXlab platform.
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Visualization of Driving Behavior Based on Hidden Feature Extraction by Using Deep Learning
IEEE Transactions on Intelligent Transportation Systems, 2017Co-Authors: Hailong Liu, Kazuhito Takenaka, Tadahiro Taniguchi, Yusuke Tanaka, Toshio BandoAbstract:In this paper, we propose a visualization method for Driving Behavior that helps people to recognize distinc- tive Driving Behavior patterns in continuous Driving Behavior data. Driving Behavior can be measured using various types of sensors connected to a control area network. The measured multi-dimensional time series data are called Driving Behavior data. In many cases, each dimension of the time series data is not independent of each other in a statistical sense. For example, accelerator opening rate and longitudinal acceleration are mutually dependent. We hypothesize that only a small number of hidden features that are essential for Driving Behavior are generating the multivariate Driving Behavior data. Thus, extracting essential hidden features from measured redundant Driving Behavior data is a problem to be solved to develop an effective visualization method for Driving Behavior. In this paper, we propose using deep sparse autoencoder (DSAE) to extract hidden features for visualization of Driving Behavior. Based on the DSAE, we propose a visualization method called a Driving color map bymapping the extracted 3-D hidden feature to the red green blue (RGB) color space. A Driving color map is produced by placing the colors in the corresponding positions on the map. The subjective experiment shows that feature extraction method based on the DSAE is effective for visualization. In addition, its performance is also evaluated numerically by using pattern recognition method. We also provide examples of applications that use Driving color maps in practical problems. In summary, it is shown the Driving color map based on DSAE facilitates better visualization of Driving Behavior.
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essential feature extraction of Driving Behavior using a deep learning method
IEEE Intelligent Vehicles Symposium, 2015Co-Authors: Hailong Liu, Kazuhito Takenaka, Tadahiro Taniguchi, Yusuke Tanaka, Takashi BandoAbstract:Driving Behavior can be represented by many different types of measured sensor information obtained through a control area network. We assume that the measured sensor information is generated from several hidden time-series data through multiple nonlinear transformations. These hidden time-series data are statistically independent of each other and capture essential Driving Behavior. Driving Behavior information is usually generated by multiple nonlinear transformations that fuse essential features, e.g., "Yaw rate" is generated by fusing the velocity of the vehicle and the change of Driving direction. However, Driving Behavior data is often redundant because such data includes multivariate information and involves duplicated essential features. In this paper, we propose a feature extraction method to extract essential features from redundant Driving Behavior data using a deep sparse autoencoder (DSAE), which is a deep learning method. Two-dimensional features are extracted from seven-dimensional artificial data using a DSAE and are determined experimentally to be highly correlated with the prepared essential features. DSAEs are also used to extract features from an actual Driving Behavior data set. To verify a DSAE's ability to extract essential Driving Behavior features and filter out redundant information, we prepare twelve data sets that include some or all of the Driving Behavior information. Twelve DSAEs are used to independently extract features from the twelve prepared data sets, and canonical correlation analysis is used to analyze the canonical correlation coefficients between extracted features. Furthermore, we verify DSAEs' ability to extract essential Driving Behavior features from the redundant Driving Behavior data sets.
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Unsupervised Hierarchical Modeling of Driving Behavior and Prediction of Contextual Changing Points
IEEE Transactions on Intelligent Transportation Systems, 2015Co-Authors: Tadahiro Taniguchi, Kazuhito Takenaka, Kentarou Hitomi, Shogo Nagasaka, Takashi BandoAbstract:An unsupervised learning method, called double articulation analyzer with temporal prediction (DAA-TP), is proposed on the basis of the original DAA model. The method will enable future advanced Driving assistance systems to determine Driving context and predict possible scenarios of Driving Behavior by segmenting and modeling incoming Driving-Behavior time series data. In previous studies, we applied the DAA model to Driving-Behavior data and argued that contextual changing points can be estimated as changing points of chunks. A sequence prediction method, which predicts the next hidden state sequence, was also proposed in a previous study. However, the original DAA model does not model the duration of chunks of Driving Behavior and is not able to do a temporal prediction of the scenarios. Our DAA-TP method explicitly models the duration of chunks of Driving Behavior on the assumption that Driving-Behavior data have a two-layered hierarchical structure, i.e., double articulation structure. For this purpose, the hierarchical Dirichlet process hidden semi-Markov model is used for explicitly modeling the duration of segments of Driving-Behavior data. A Poisson distribution is also used to model the duration distribution of Driving-Behavior segments. The duration distribution of chunks of Driving-Behavior data is also theoretically calculated using the reproductive property of the Poisson distribution. We also propose a calculation method for obtaining the probability distribution of the remaining duration of current Driving words as a mixture of Poisson distribution with a theoretical approximation for unobserved Driving words. This method can calculate the posterior probability distribution of the next termination time of chunks by explicitly modeling all probable chunking results for observed data. The DAA-TP was applied to a synthetic data set having a double articulation structure to evaluate its model consistency. To evaluate the effectiveness of DAA-TP, we applied it to a Driving-Behavior data set recorded at actual factory circuits. The DAA-TP could predict the next termination time of chunks more accurately than the compared methods. We also report the qualitative results for understanding the potential capability of DAA-TP.
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drive video summarization based on double articulation structure of Driving Behavior
ACM Multimedia, 2012Co-Authors: Kazuhito Takenaka, Takashi Bando, Shogo Nagasaka, Tadahiro TaniguchiAbstract:This paper provides a novel summarization method for drive videos using Driving Behavior, such as driver maneuvers and vehicle reaction, recorded simultaneously alongside video. We segmented the Driving Behavior into chunks via an unsupervised manner and summarized the drive videos using the chunks, i.e., the switching points of the chunks were emphasized and the middle of the chunks were compressed. As the result of subjective evaluation, we found that the chunks were more consistent with human-recognized Driving context than the image-based method and that the summarized video was more suitable for reviewing entire Driving scenes, i.e., our method achieved an efficient summarization.
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semiotic prediction of Driving Behavior using unsupervised double articulation analyzer
IEEE Intelligent Vehicles Symposium, 2012Co-Authors: Tadahiro Taniguchi, Kentarou Hitomi, Shogo Nagasaka, Naiwala P Chandrasiri, Takashi BandoAbstract:In this paper, we propose a novel semiotic prediction method for Driving Behavior based on double articulation structure. It has been reported that predicting Driving Behavior from its multivariate time series Behavior data by using machine learning methods, e.g., hybrid dynamical system, hidden Markov model and Gaussian mixture model, is difficult because a driver's Behavior is affected by various contextual information. To overcome this problem, we assume that contextual information has a double articulation structure and develop a novel semiotic prediction method by extending nonparametric Bayesian unsupervised morphological analyzer. Effectiveness of our prediction method was evaluated using synthetic data and real Driving data. In these experiments, the proposed method achieved long-term prediction 2–6 times longer than some conventional methods.
Moshe Benakiva - One of the best experts on this subject based on the ideXlab platform.
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estimation of an integrated Driving Behavior model
Transportation Research Part C-emerging Technologies, 2009Co-Authors: Tomer Toledo, Haris N Koutsopoulos, Moshe BenakivaAbstract:This paper presents the methodology and results of estimation of an integrated Driving Behavior model that attempts to integrate various Driving decisions. The model explains lane changing and acceleration decisions jointly and so, captures inter-dependencies between these Behaviors and represents drivers' planning capabilities. It introduces new models that capture drivers' choice of a target gap that they intend to use in order to change lanes, and acceleration models that capture drivers' Behavior to facilitate the completion of a desired lane change using the target gap. The parameters of all components of the model are estimated simultaneously with the maximum likelihood method and using detailed vehicle trajectory data collected in a freeway section in Arlington, Virginia. The estimation results are presented and discussed in detail.
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integrated Driving Behavior modeling
Transportation Research Part C-emerging Technologies, 2007Co-Authors: Tomer Toledo, Haris N Koutsopoulos, Moshe BenakivaAbstract:This paper develops, implements and tests a framework for Driving Behavior modeling that integrates the various decisions, such as acceleration, lane changing and gap acceptance. Furthermore, the proposed framework is based on the concepts of short-term goal and short-term plan. Drivers are assumed to conceive and perform short-term plans in order to accomplish short-term goals. This Behavioral framework supports a more realistic representation of the Driving task, since it captures drivers' planning capabilities and allows decisions to be based on anticipated future conditions. An integrated Driving Behavior model, which utilizes these concepts, is developed. The model captures both lane changing and acceleration Behaviors. The driver's short-term goal is defined by the target lane. Drivers who wish to change lanes but cannot change lanes immediately, select a short-term plan to perform the desired lane change. Short-term plans are defined by the various gaps in traffic in the target lane. Drivers adapt their acceleration Behavior to facilitate the lane change using the target gap. Hence, inter-dependencies between lane changing and acceleration Behaviors are captured.
Kazuhito Takenaka - One of the best experts on this subject based on the ideXlab platform.
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Visualization of Driving Behavior Based on Hidden Feature Extraction by Using Deep Learning
IEEE Transactions on Intelligent Transportation Systems, 2017Co-Authors: Hailong Liu, Kazuhito Takenaka, Tadahiro Taniguchi, Yusuke Tanaka, Toshio BandoAbstract:In this paper, we propose a visualization method for Driving Behavior that helps people to recognize distinc- tive Driving Behavior patterns in continuous Driving Behavior data. Driving Behavior can be measured using various types of sensors connected to a control area network. The measured multi-dimensional time series data are called Driving Behavior data. In many cases, each dimension of the time series data is not independent of each other in a statistical sense. For example, accelerator opening rate and longitudinal acceleration are mutually dependent. We hypothesize that only a small number of hidden features that are essential for Driving Behavior are generating the multivariate Driving Behavior data. Thus, extracting essential hidden features from measured redundant Driving Behavior data is a problem to be solved to develop an effective visualization method for Driving Behavior. In this paper, we propose using deep sparse autoencoder (DSAE) to extract hidden features for visualization of Driving Behavior. Based on the DSAE, we propose a visualization method called a Driving color map bymapping the extracted 3-D hidden feature to the red green blue (RGB) color space. A Driving color map is produced by placing the colors in the corresponding positions on the map. The subjective experiment shows that feature extraction method based on the DSAE is effective for visualization. In addition, its performance is also evaluated numerically by using pattern recognition method. We also provide examples of applications that use Driving color maps in practical problems. In summary, it is shown the Driving color map based on DSAE facilitates better visualization of Driving Behavior.
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essential feature extraction of Driving Behavior using a deep learning method
IEEE Intelligent Vehicles Symposium, 2015Co-Authors: Hailong Liu, Kazuhito Takenaka, Tadahiro Taniguchi, Yusuke Tanaka, Takashi BandoAbstract:Driving Behavior can be represented by many different types of measured sensor information obtained through a control area network. We assume that the measured sensor information is generated from several hidden time-series data through multiple nonlinear transformations. These hidden time-series data are statistically independent of each other and capture essential Driving Behavior. Driving Behavior information is usually generated by multiple nonlinear transformations that fuse essential features, e.g., "Yaw rate" is generated by fusing the velocity of the vehicle and the change of Driving direction. However, Driving Behavior data is often redundant because such data includes multivariate information and involves duplicated essential features. In this paper, we propose a feature extraction method to extract essential features from redundant Driving Behavior data using a deep sparse autoencoder (DSAE), which is a deep learning method. Two-dimensional features are extracted from seven-dimensional artificial data using a DSAE and are determined experimentally to be highly correlated with the prepared essential features. DSAEs are also used to extract features from an actual Driving Behavior data set. To verify a DSAE's ability to extract essential Driving Behavior features and filter out redundant information, we prepare twelve data sets that include some or all of the Driving Behavior information. Twelve DSAEs are used to independently extract features from the twelve prepared data sets, and canonical correlation analysis is used to analyze the canonical correlation coefficients between extracted features. Furthermore, we verify DSAEs' ability to extract essential Driving Behavior features from the redundant Driving Behavior data sets.
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Unsupervised Hierarchical Modeling of Driving Behavior and Prediction of Contextual Changing Points
IEEE Transactions on Intelligent Transportation Systems, 2015Co-Authors: Tadahiro Taniguchi, Kazuhito Takenaka, Kentarou Hitomi, Shogo Nagasaka, Takashi BandoAbstract:An unsupervised learning method, called double articulation analyzer with temporal prediction (DAA-TP), is proposed on the basis of the original DAA model. The method will enable future advanced Driving assistance systems to determine Driving context and predict possible scenarios of Driving Behavior by segmenting and modeling incoming Driving-Behavior time series data. In previous studies, we applied the DAA model to Driving-Behavior data and argued that contextual changing points can be estimated as changing points of chunks. A sequence prediction method, which predicts the next hidden state sequence, was also proposed in a previous study. However, the original DAA model does not model the duration of chunks of Driving Behavior and is not able to do a temporal prediction of the scenarios. Our DAA-TP method explicitly models the duration of chunks of Driving Behavior on the assumption that Driving-Behavior data have a two-layered hierarchical structure, i.e., double articulation structure. For this purpose, the hierarchical Dirichlet process hidden semi-Markov model is used for explicitly modeling the duration of segments of Driving-Behavior data. A Poisson distribution is also used to model the duration distribution of Driving-Behavior segments. The duration distribution of chunks of Driving-Behavior data is also theoretically calculated using the reproductive property of the Poisson distribution. We also propose a calculation method for obtaining the probability distribution of the remaining duration of current Driving words as a mixture of Poisson distribution with a theoretical approximation for unobserved Driving words. This method can calculate the posterior probability distribution of the next termination time of chunks by explicitly modeling all probable chunking results for observed data. The DAA-TP was applied to a synthetic data set having a double articulation structure to evaluate its model consistency. To evaluate the effectiveness of DAA-TP, we applied it to a Driving-Behavior data set recorded at actual factory circuits. The DAA-TP could predict the next termination time of chunks more accurately than the compared methods. We also report the qualitative results for understanding the potential capability of DAA-TP.
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drive video summarization based on double articulation structure of Driving Behavior
ACM Multimedia, 2012Co-Authors: Kazuhito Takenaka, Takashi Bando, Shogo Nagasaka, Tadahiro TaniguchiAbstract:This paper provides a novel summarization method for drive videos using Driving Behavior, such as driver maneuvers and vehicle reaction, recorded simultaneously alongside video. We segmented the Driving Behavior into chunks via an unsupervised manner and summarized the drive videos using the chunks, i.e., the switching points of the chunks were emphasized and the middle of the chunks were compressed. As the result of subjective evaluation, we found that the chunks were more consistent with human-recognized Driving context than the image-based method and that the summarized video was more suitable for reviewing entire Driving scenes, i.e., our method achieved an efficient summarization.
Tomer Toledo - One of the best experts on this subject based on the ideXlab platform.
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can providing feedback on Driving Behavior and training on parental vigilant care affect male teen drivers and their parents
Accident Analysis & Prevention, 2014Co-Authors: Haneen Farah, Tomer Toledo, Oren Musicant, Yaara Shimshoni, Einat Grimberg, Haim Omer, Tsippy LotanAbstract:This study focuses on investigating the Driving Behavior of young novice male drivers during the first year of Driving (three months of accompanied Driving and the following nine months of solo Driving). The study's objective is to examine the potential of various feedback forms on Driving to affect young drivers' Behavior and to mitigate the transition from accompanied to solo Driving. The study examines also the utility of providing parents with guidance on how to exercise vigilant care regarding their teens' Driving. Driving Behavior was evaluated using data collected by In-Vehicle Data Recorders (IVDR), which document events of extreme g-forces measured in the vehicles. IVDR systems were installed in 242 cars of the families of young male drivers, however, only 217 families of young drivers aged 17-22 (M=17.5; SD=0.8) completed the one year period. The families were randomly allocated into 4 groups: (1) Family feedback: In which all the members of the family were exposed to feedback on their own Driving and on that of the other family members; (2) Parental training: in which in addition to the family feedback, parents received personal guidance on ways to enhance vigilant care regarding their sons' Driving; (3) Individual feedback: In which family members received feedback only on their own Driving Behavior (and were not exposed to the data on other family members); (4) Control: Group that received no feedback at all. The feedback was provided to the different groups starting from the solo period, thus, the feedback was not provided during the supervised period. The data collected by the IVDRs was first analyzed using analysis of variance in order to compare the groups with respect to their monthly event rates. Events' rates are defined as the number of events in a trip divided by its duration. This was followed by the development and estimation of random effect negative binomial models that explain the monthly event rates of young drivers and their parents. The study showed that: (1) the Parental training group recorded significantly lower events rates (-29%) compared to the Control group during the solo period; (2) although directed mainly at the novice drivers, the intervention positively affected also the Behavior of parents, with both fathers and mothers in the Parental training group improving their Driving (by -23% for both fathers and mothers) and mothers improving it also in the Family feedback group (by -30%). Thus, the intervention has broader impact effect beside the targeted population. It can be concluded that providing feedback on Driving Behavior and parental training in vigilant care significantly improves the Driving Behavior of young novice male drivers. Future research directions could include applying the intervention to a broader population, with larger diversity with respect to their Driving records, culture, and Behaviors. The challenge is to reach wide dissemination of IVDR for young drivers accompanied by parents' involvement, and to find the suitable incentives for its sustainability. Language: en
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estimation of an integrated Driving Behavior model
Transportation Research Part C-emerging Technologies, 2009Co-Authors: Tomer Toledo, Haris N Koutsopoulos, Moshe BenakivaAbstract:This paper presents the methodology and results of estimation of an integrated Driving Behavior model that attempts to integrate various Driving decisions. The model explains lane changing and acceleration decisions jointly and so, captures inter-dependencies between these Behaviors and represents drivers' planning capabilities. It introduces new models that capture drivers' choice of a target gap that they intend to use in order to change lanes, and acceleration models that capture drivers' Behavior to facilitate the completion of a desired lane change using the target gap. The parameters of all components of the model are estimated simultaneously with the maximum likelihood method and using detailed vehicle trajectory data collected in a freeway section in Arlington, Virginia. The estimation results are presented and discussed in detail.
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integrated Driving Behavior modeling
Transportation Research Part C-emerging Technologies, 2007Co-Authors: Tomer Toledo, Haris N Koutsopoulos, Moshe BenakivaAbstract:This paper develops, implements and tests a framework for Driving Behavior modeling that integrates the various decisions, such as acceleration, lane changing and gap acceptance. Furthermore, the proposed framework is based on the concepts of short-term goal and short-term plan. Drivers are assumed to conceive and perform short-term plans in order to accomplish short-term goals. This Behavioral framework supports a more realistic representation of the Driving task, since it captures drivers' planning capabilities and allows decisions to be based on anticipated future conditions. An integrated Driving Behavior model, which utilizes these concepts, is developed. The model captures both lane changing and acceleration Behaviors. The driver's short-term goal is defined by the target lane. Drivers who wish to change lanes but cannot change lanes immediately, select a short-term plan to perform the desired lane change. Short-term plans are defined by the various gaps in traffic in the target lane. Drivers adapt their acceleration Behavior to facilitate the lane change using the target gap. Hence, inter-dependencies between lane changing and acceleration Behaviors are captured.
